Machine learning systems and methods for predictive engagement

ABSTRACT

A machine learning (ML) process can include teaching, with a teaching set, a first ML algorithm to generate one or more machine-predicted results. One or more weights can be generated based on the one or more machine-predicted results and the teaching set. A second ML algorithm can be generated based on the one or more weights. Via the second ML algorithm, one or more machine-learned results can be generated. A description of one or more candidates can be received. Based on the one or more machine-learned results, a respective likelihood of interest in a CCG class of positions for each of the one or more candidates can be generated. A respective communication can be transmitted to each of a subset of the one or more candidates open to the respective likelihood of interest in the CCG class of positions for the subset above a threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/063,263, filed Oct. 5, 2020, entitled “MACHINE LEARNING SYSTEMS ANDMETHODS FOR PREDICTIVE ENGAGEMENT,” which claims the benefit of andpriority to U.S. Patent Application No. 62/910,644, filed Oct. 4, 2019,entitled “MACHINE LEARNING SYSTEMS AND METHODS FOR PREDICTIVE TARGETINGAND ENGAGEMENT,” each of which are incorporated herein by reference intheir entireties.

BACKGROUND

Previous approaches to predicting CCG affiliation have relied uponself-identification. However, not all those attracted to CCG-basedpositions will self-identify, and, of those that do self-identify, termsand phrases used to indicate CCG affiliation can be highly variant andsuch indications can become invalid over time as an affiliation maychange. Furthermore, reliance on self-identification may render previousapproaches incapable of predicting whether or not an applicant is likelyto respond to communications for CCG-based positions. Accordingly, thereexists an unmet need for systems and methods that can more accuratelypredict non-self-identifying applicants that are of a particular desiredstatus.

BRIEF SUMMARY OF THE DISCLOSURE

Briefly described, and according to one embodiment, aspects of thepresent disclosure generally relate to systems and methods forevaluating and targeting entities.

In at least on embodiment, the present system is configured to generatepredictions, such as a likelihood of a candidate to be interested incontract, contingent, or gig (CCG)-based employment. In variousembodiments, the present system may implement various machine learningtechniques, natural language generation (NLG) practices, and datanormalization processes to analyze data and generate predictions. In oneembodiment, the analyzed data may include candidate data, position data,recruitment data, and other information. In at least one embodiment, thepredictions may include, but are not limited to, predictions for whetheror not a candidate is likely to be a CCG-based position holder, whetheror not a candidate is likely to accept a CCG-based position for theirnext job, and whether or not a candidate is likely to engage withrecruitment communications related to CCG-based positions. In one ormore embodiments, the natural language generation techniques mayinclude, but are not limited to, processing data and identifyinglanguage that is likely to elicit engagement from a candidate whenincluded in candidate communications related to CCG-based positions, andautomatically generating communications (or components thereof) based onidentified language.

In at least one embodiment, the present system is configured toautomatically (or in response to input) collect, retrieve, or accessdata. In various embodiments, the system is configured to automaticallyscrape and index publicly accessible data sources to obtain candidatedata, position data, recruitment data, and/or other information. In oneor more embodiments, the system is configured to automatically accessand process candidate data, position data, recruitment data, and/orother information stored in one or more databases operatively connectedto the system. In various embodiments, the system retrieves data byprocessing electronic documents, web pages, and other digital media. Insome embodiments, the system processes resumes, position descriptions,online reviews, and other digital media to obtain candidate, position,and/or recruitment data, or other information.

In one or more embodiments, the present system may normalize data,thereby rendering the data more suitable for analysis via machinelearning methods and other techniques described herein. In at least oneembodiment, the present system may clean and normalize data to remove,impute, or otherwise modify missing, null, or erroneous data values. Invarious embodiments, the present system may perform entity resolution todisambiguate and correlate any data to be indexed and/or analyzedaccording to the processes described herein. In at least one embodiment,because companies, recruiters, candidates, and other entities may usedisparate terms to refer to similar positions, job titles, and othercriteria, performing entity resolution may allow the present system topresent a candidate, recruiter, or another user with positions, jobtitles, and other information that were otherwise unknown or consideredirrelevant to thereto.

In one or more embodiments, the present system may include one or moremachine learning models. In various embodiments, the present system mayiteratively generate, modify, and train machine learning models toperform actions including, but not limited to, predicting whether or nota candidate is likely to be a CCG-based position holder, predictingwhether or not a candidate is likely to accept a CCG-based position fortheir next job, and predicting whether or not a candidate is likely toengage with recruitment communications related to CCG-based positions.In at least one embodiment, predictions generated by one or more machinelearning models may be binary (e.g., exemplary predictions being“candidate X is a CCG-based position holder,” and “candidate Y is not aCCG-based position holder), or may be correlated to a scale (e.g.,exemplary predictions being “candidate X is most likely to be aCCG-based position holder,” and candidate Y is less likely to be aCCG-based position holder”). In one or more embodiments, predictions maybe formatted as classifications determined and assigned based oncomparisons between prediction scores (generated by machine learningmodels) and prediction thresholds that may be predefined and/orgenerated according to one or more machine learning models.

In various embodiments, the system may generate or receive training setsfor training (also referred to as “teaching”) machine learning models.In at least one embodiment, the system may generate or receive CCGstatus training sets for predicting whether or not, or to what degree, acandidate is likely to be a CCG-based position holder. For example, thesystem may generate a CCG status training set including data describingboth known CCG-based position holders and known non-CCG position holders(e.g., for example, known full-time position holders). In the sameexample, the system may use the CCG status training set to generate andtrain one or more machine learning models to accurately and preciselypredict a likelihood of a candidate being a current and/or pastCCG-based position holder.

In one or more embodiments, the system may generate or receive CCGlikelihood training sets for predicting whether or not, or to whatdegree, a candidate is likely to accept a CCG-based position for theirnext employment source. For example, the system may generate a CCGlikelihood training set including data describing candidates thatelected a CCG-based position for their most recent employment source. Inthe same example, the system may use the CCG likelihood training set togenerate and train one or more machine learning models to accurately andprecisely predict a likelihood of a candidate accepting a CCG-basedposition for their next employment source.

In various embodiments, the system may generate or receive CCGengagement training sets for predicting whether or not, or to whatdegree, a candidate is likely to engage with recruitment communicationsfor CCG-based positions. For example, the system may generate a CCGengagement training set including data describing candidates, historicCCG-related communications, and historic engagement of the candidateswith CCG-related recruitment communications. In the same example, thesystem may use the CCG engagement training set to generate and train oneor more machine learning models to accurately and precisely predict alikelihood of a candidate engaging with a CCG-related recruitmentcommunication.

In at least one embodiment, the system may perform NLG and machinelearning processes to identify and predict language that is most likelyto elicit engagement from one or more candidates. In one or moreembodiments, the system may track engagement with transmittedcommunications to augment NLG processes and generate improvedcommunication language. In various embodiments, the system may perform afirst set of machine learning processes to identify candidates mostlikely to respond to a CCG-related recruitment communication, and mayperform a second set of machine learning processes and NLG processes toidentify and generate CCG-related communications (or elements thereof)that are most likely to elicit engagement if transmitted to thecandidates identified in the first set of machine learning processes.

In one or more embodiments, the present system may be implemented toevaluate current position holders within a company, institution, etc. Inat least one embodiment, the system may be used for retention analysisof non-CCG position holders at a company. In various embodiments, thesystem may be configured to identify, via machine learning methods, asubset of the non-CCG position holders that are likely to accept aCCG-based position for their next employment source. In one or moreembodiments, the system may also identify one or more machine learningparameters (e.g., portions of data, information, etc.) that are mostinfluential in determining likelihood of a non-CCG position holderaccepting a CCG-based position. In at least one embodiment, the presentsystem may be used to identify and predict trends for supply and demandof non-CCG and CCG-based positions, and identify potential candidates tomeet supply and demand trends. For example, an embodiment of the systemmay be configured to identify, for a particular company, current non-CCGand/or CCG-based position holders that are likely to engage withrecruitment communications and accept a position (non-CCG or CCG-based)outside of the particular company for their next employment source. Inthe same example, the system may also be configured to identifycandidates that are, qualified to replace a current non-CCG or CCG-basedposition holder, likely to be a CCG-based position holder, likely toaccept a CCG-based position for their next employment source, and/orlikely to engage with CCG-related recruitment communications.

According to a first aspect, a machine learning process comprising: A)teaching, with a teaching set, at least one first machine learningalgorithm to generate one or more machine predicted results; B)generating one or more weights based on the one or more machinepredicted results and the teaching set; C) generating at least onesecond machine learning algorithm based on the one or more weights; D)generating, via the at least one second machine learning algorithm, oneor more machine-learned results; E) receiving a description of one ormore candidates; F) generating a respective likelihood of interest in aCCG class of positions for each of the one or more candidates based onthe one or more machine-learned results; and G) generating a respectivecommunication to each of a subset of the one or more candidates open tothe respective likelihood of interest in the CCG class of positions forthe subset above a threshold.

According to a second aspect, the machine learning process of the firstaspect or any other aspect, further comprising: A) receiving a set ofcandidate parameters for a particular position, the particular positioncorresponding to the CCG class of positions; and B) processing the setof candidate parameters to identify one or more candidates from a set ofcandidates that meet the set of candidate parameters.

According to a third aspect, the machine learning process of the secondaspect or any other aspect, further comprising: A) generating a rankingof the subset of the one or more candidates based on the respectivelikelihood of interest in the CCG class of positions; and B) generatinga communication based on the ranking of the subset.

According to a fourth aspect, the machine learning process of the firstaspect or any other aspect, further comprising: A) generating particularlanguage designed to provoke a response from each of the subset of theone or more candidates; and B) generating one or more strings of textvia natural language processing for the respective communication foreach of the subset of the one or more candidates, wherein the one ormore strings of text comprise language are based on the particularlanguage.

According to a fifth aspect, the machine learning process of the firstaspect or any other aspect, wherein the teaching set describes one ormore first communications with a known positive result and one or moresecond communications with a known negative result.

According to a sixth aspect, the machine learning process of the firstaspect or any other aspect, or any other claim further comprising: A)receiving an indication that a particular candidate does not prefer theCCG class of positions; B) subsequent to receiving the indication,generating a change in a profile associated with the particularcandidate; C) generating that the change in the profile increases alikelihood of interest in the CCG class of positions more than or equalto a threshold amount; and D) in response to the change increasing thelikelihood of interest more than or equal to the threshold amount,adjusting the profile to facilitate communication with the particularcandidate.

According to a seventh aspect, the machine learning process of the firstaspect or any other aspect, wherein the description of the one or morecandidates is extracted from at least one of media and investigativeinformation.

According to an eighth aspect, a machine learning system comprising: A)memory comprising a teaching set describing one or more firstcommunications with a known positive result and one or more secondcommunications with a known negative result; and B) at least one devicein communication with the memory, the at least one device beingconfigured to: 1) teach, with a teaching set, at least one first machinelearning algorithm to generate one or more machine predicted results; 2)analyze one or more weights based on the one or more machine predictedresults and the teaching set; 3) generate at least one second machinelearning algorithm based on the one or more weights; 4) generate, viathe at least one second machine learning algorithm, one or moremachine-learned results; and 5) analyze a respective likelihood ofinterest in a CCG class of positions for each of one or more candidatesbased on the one or more machine-learned results.

According to a ninth aspect, the machine learning system of the eighthaspect or any other aspect, wherein the at least one device is furtherconfigured to exclude any candidates from the one or more candidatesthat does not meet a predefined threshold.

According to a tenth aspect, the machine learning system of the eighthaspect or any other aspect, wherein the at least one device is furtherconfigured to generate a respective communication to each of a subset ofthe one or more candidates open to the respective likelihood of interestin the CCG class of positions for the subset above a threshold.

According to an eleventh aspect, the machine learning system of thetenth aspect or any other aspect, wherein the at least one device isfurther configured to: A) analyze a respective result associated withthe respective communication for each of the subset of the one or morecandidates; and B) transform the teaching set based on the respectiveresult for each of the subset of the one or more candidates.

According to a twelfth aspect, a machine learning system comprising: A)memory; and B) at least one device in communication with the memory, theat least one device being configured to: 1) teach, with a teaching set,at least one first machine learning algorithm to generate one or moremachine predicted results; 2) analyze one or more weights based on theone or more machine predicted results and the teaching set; 3) generateat least one second machine learning algorithm based on the one or moreweights; 4) generate, via the at least one second machine learningalgorithm, one or more machine-learned results; 5) analyze a respectivelikelihood of interest in a CCG class of positions for each of one ormore candidates based on the one or more machine-learned results; and 6)generate a respective communication to each of a subset of the one ormore candidates open to the respective likelihood of interest in the CCGclass of positions for the subset above a threshold.

According to a thirteenth aspect, the machine learning system of thetwelfth aspect or any other aspect, wherein the at least one device isfurther configured to: A) analyze a respective result associated withthe respective communication for each of the subset of the one or morecandidates; and B) transform the teaching set based on the respectiveresult for each of the subset of the one or more candidates.

According to a fourteenth aspect, the machine learning system of thethirteenth aspect or any other aspect, wherein the at least one deviceis further configured to: A) transform, with the transformed teachingset, the at least one first machine learning algorithm to generate atransformed one or more machine predicted results; and B) analyze one ormore transformed weights based on the transformed one or more machinepredicted results and the transformed teaching set.

According to a fifteenth aspect, the machine learning system of thefourteenth aspect or any other aspect, wherein the at least one deviceis further configured to: A) generate at least one transformed secondmachine learning algorithm based on the one or more transformed weights;B) generate, via the at least one transformed second machine learningalgorithm, one or more additional machine-learned results; and C)identify a transformed respective likelihood of interest in the CCGclass of positions for each of the one or more candidates based on theone or more additional machine-learned results.

According to a sixteenth aspect, the machine learning system of thetwelfth aspect or any other aspect, wherein the at least one device isfurther configured to: A) receive a set of candidate parameters for aparticular position, the particular position corresponding to the CCGclass of positions; and B) process the set of candidate parameters toidentify a candidate subset of the one or more candidates that meets theset of candidate parameters, wherein the subset of the one or morecandidates are selected from the candidate subset.

According to a seventeenth aspect, the machine learning system of thetwelfth aspect or any other aspect, wherein the at least one device isfurther configured to: A) receive the respective likelihood of interestin the CCG class of positions for the one or more candidates; B)analyze, for each candidate, if the respective likelihood of interestfor a subset of the one or more candidates meets a threshold for aparticular position; and C) generate and transmit, to a profileassociated with the particular position, a description of the subset ofthe one or more candidates that meet the threshold.

According to an eighteenth aspect, the machine learning system of thetwelfth aspect or any other aspect, wherein the at least one device isfurther configured to exclude any candidates from the one or morecandidates that does not meet a predefined threshold.

These and other aspects, features, and benefits of the claimed systemsand methods will become apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings illustrate one or more embodiments and/oraspects of the disclosure and, together with the written description,serve to explain the principles of the disclosure. Wherever possible,the same reference numbers are used throughout the drawings to refer tothe same or like elements of an embodiment, and wherein:

FIG. 1 shows an exemplary targeting system, according to one embodimentof the present disclosure;

FIG. 2 shows an exemplary prediction process, according to oneembodiment of the present disclosure;

FIG. 3 shows an exemplary machine learning process, according to oneembodiment of the present disclosure; and

FIG. 4 shows an exemplary communication process, according to oneembodiment of the present disclosure.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will, nevertheless, be understood that nolimitation of the scope of the disclosure is thereby intended; anyalterations and further modifications of the described or illustratedembodiments, and any further applications of the principles of thedisclosure as illustrated therein are contemplated as would normallyoccur to one skilled in the art to which the disclosure relates. Alllimitations of scope should be determined in accordance with and asexpressed in the claims.

Whether a term is capitalized is not considered definitive or limitingof the meaning of a term. As used in this document, a capitalized termshall have the same meaning as an uncapitalized term, unless the contextof the usage specifically indicates that a more restrictive meaning forthe capitalized term is intended. However, the capitalization or lackthereof within the remainder of this document is not intended to benecessarily limiting unless the context clearly indicates that suchlimitation is intended.

As used herein, “applicant” or “candidate” generally refers to anyindividual and is not limited to those individuals that have applied toa particular position or role. For example, an applicant can include anindividual in control of a user account on a social media platform, anindividual at a particular company, an individual possessing aparticular skillset or experience, or an individual currently orformerly residing at a particular location or working in a particularindustry.

In at least one embodiment, data may be excluded from processesdescribed herein in order to adhere to one or more of governmentregulations, corporate policies, data consent agreements, privacyregulations, and other policies addressing data privacy. In someembodiments, a corporate policy may mandate that personal data beomitted from processes described herein. In at least one embodiment, thepresent system may exclude, from the processes, personal data including,but not limited to, financial health, debt obligations, marital status,age, social media activities, and other data. In further embodiments, agovernment regulation may mandate that personal data be anonymized. Instill further embodiments, the present system may execute one or moreanonymization processes on personal data in adherence to the governmentregulation.

Overview

In various embodiments, the present system may implement various machinelearning techniques, NLG practices, and data normalization processes toanalyze data and generate predictions. In one embodiment, the analyzeddata may include candidate data, position data, recruitment data, andother information. In at least one embodiment, the predictions mayinclude, but are not limited to, predictions for whether or not acandidate is likely to be a contingent, contract, and/or gig (CCG)-basedposition holder, predictions for whether or not a candidate is likely toaccept a CCG-based position for their next job, and predictions forwhether or not a candidate is likely to engage with recruitmentcommunications related to CCG-based positions. In one or moreembodiments, the NLG techniques may include, but are not limited to,processing data and identifying language that is likely to elicitengagement from a candidate when included in candidate communicationsrelated to CCG-based positions, and automatically generatingcommunications (or components thereof) based on identified language.

In at least one embodiment, the present system may automatically ormanually (e.g., in response to input) collect, retrieve, or access dataincluding, but not limited to, candidate data, recruitment data, andposition data. In one or more embodiments, the system may collect databy a plurality of methods including, but not limited to, initiatingrequests at data sources (e.g., via an application programming interface(API)), scraping and indexing webpages and other information sources,retrieving data from a data store, and receiving and processing inputsor other uploaded information (e.g., such as an uploaded resume,position offer, advertisement, etc.). In one example, to collectrecruitment data, the system receives and processing a set of inputs anduploads from a particular user account with which a recruiting agency isassociated.

In various embodiments, the system may continuously and/or automaticallymonitor data sources for changes in candidate data and otherinformation. In at least one embodiment, upon detecting a change incandidate data or other information, the system may perform actionsincluding, but not limited to, automatically collecting, storing, andorganizing the changed candidate data or other information, generatingand transmitting alerts to one or more entities (e.g., recruiters,companies, etc.) indicating the change in candidate data and/or animpact thereof on an associated candidate's likelihood to be a currentCCG-based position holder, likelihood to accept a CCG-based position,and/or likelihood to engage with CCG-related recruitment communications,re-training one or more machine learning models to account for thechanged data, and re-executing one or more machine learning processes togenerate updated predictions and classifications based on the changeddata.

In at least one embodiment, the present system may perform machinelearning techniques to generate predictions of candidate alignment,behavior, and decisions. In one or more embodiments, machine learningmethods may include, but are not limited to, neural networks, gradientboosting algorithms, mutual information classifiers, random forestclassification, and other machine learning techniques and relatedalgorithms. In various embodiments, machine learning model generation,execution, and training may be performed according to one or moreequations. For example, iterative machine learning model generation andtraining may be performed according to Equation 1, in which E(x_(ijg))represents the ensemble model, f_(k)(x) represents an individual model,and x_(ijg) represents a vector of characteristics for a candidate, i,working at company, j, in a role, g.

E(x _(ijg))=E(f ₁(x _(ijg)), . . . ,f _(n)(x _(ijg)))  (Equation 1)

As another example, machine learning predictions may be formatted asclassifications according to methods similar to methods implemented inEquation 2, in which h(x_(ijg)) is a machine-learned prediction from theone or more machine-learned predictions, h₀ is a predefined “non-CCG”threshold, h₁ is a predefined “potentially CCG” threshold, h₂ is apredefined “likely CCG” threshold, c(x_(ijg)) is the classification towhich each one the one or more machine-learned predictions is assigned.

$\begin{matrix}{{c\left( x_{ijg} \right)} = \left\{ \begin{matrix}{{{candidate}{is}{least}{likely}{to}{be}{CCG}{if}{h\left( x_{ijg} \right)}} < h_{0}} \\{{{candidate}{may}{be}{CCG}{}{if}h_{0}} < {h\left( x_{ijg} \right)} < h_{1}} \\{{{candidate}{is}{more}{likely}{to}{be}{CCG}h_{1}} < {h\left( x_{ijg} \right)} < h_{2}} \\{{{candidate}{is}{most}{likely}{to}{be}{CCG}{h\left( x_{ijg} \right)}} > h_{2}}\end{matrix} \right.} & \left( {{Equation}2} \right)\end{matrix}$

The machine learning models may leverage one or more algorithms toevaluate, analyze, and classify data inputs, and generate and classifyoutputs. For example, the system may include algorithms including, butnot limited to, one or more contingent, contract, and/or gig (CCG)-basedposition holder likelihood algorithms, one or more CCG-based positionacceptance likelihood algorithms, one or more CCG-based communicationengagement likelihood algorithms, one or more likelihood to be acontingent position holder algorithms, one or more likelihood to be acontracted position holder algorithms, one or more likelihood to be agig-based position holder algorithms, one or more likelihood to accept acontingent position algorithms, one or more likelihood to accept acontract position algorithms, and one or more likelihood to accept agig-based position algorithms.

It will be understood to one of ordinary skill in the art that nolimitation is placed on sequencing of machine learning and/or NLGprocesses, and any combination, sequence, and configuration of machinelearning and/or NLG processes may be formulated without departing fromthe spirit and scope of the present disclosure. In various embodiments,the system may only perform machine learning processes related topredicting CCG-based position holder status and CCG-relatedcommunication engagement. In one or more embodiments, the system mayonly perform machine learning processes related to predicting CCG-basedposition acceptance and CCG-related communication engagement. In atleast one embodiment, the system may perform machine learning processesrelated to predicting: 1) only contingent, only contract, or onlygig-based position holder status; 2) only contingent, only contract, oronly gig-based position acceptance; 3) only contingent, only contract,or only gig-related communication engagement; 4) overall communicationengagement (e.g., likelihood to engage with any recruitment-relatedcommunication; 5) only full-time-related communication engagement; 6)only CCG-related communication engagement; and 7) engagement with aparticular set of communication language; 8) engagement withcommunications delivered via a particular communication method (e.g.,emails to a personal email address, messages to a LinkedIn™ profile,text messages, etc.); and 9) engagement with communications having oneor more communication parameters (e.g., communications delivered before9 AM, communications with particular subject lines, etc.).

In at least one embodiment, the present system may identify candidatesthat demonstrate hybridity in their employment source selections. Invarious embodiments, hybridity, as described herein, may refer to aquality of performing both CCG-based and non-CCG-based roles. Forexample, a candidate with an employment history that includes apreviously-held non-CCG-based position, such as a taxi driving position,and a currently-held CCG-based position, such as an Uber™ drivingposition, may be found to demonstrate hybridity in their employmentsource selections. In one or more embodiments, the present system mayanalyze hybridity-demonstrating candidates to identify circumstancesand/or criteria that may be predictive of when a candidate may choose aparticular employment source type (e.g., non-CCG employment) overanother employment source type (e.g., CCG-based employment). Forexample, an embodiment of the system may process candidate data (andother information) and identify that a candidate has previously heldboth non-CCG and CCG-based positions. In the same example, the systemmay analyze the candidate data via machine learning methods to identifyparameters and/or data patterns that appear predictive for thecandidate's choice of employment source type. In an exemplary scenario,the system may determine that the candidate is most likely to accept aCCG-based position for their next employment source, if the CCG-basedposition offers an hourly gig-based wage that amounts to at least 120%of an effective non-CCG hourly wage being currently collected by thecandidate (e.g., as a salary). In the same exemplary scenario, thesystem may determine that the candidate is most likely to accept anon-CCG position over a CCG-based position when the non-CCG positionincludes opportunities for remote work. Continuing the same exemplaryscenario, the hybridity analysis may inform an organization currentlyemploying the candidate on potential methods for retaining the employee(e.g., by offering remote work, increasing effective hourly wage orsalary, etc.).

In one or more embodiments, by identifying driving factors behindcandidate employment source decisions, the system may allow a recruiterto more specifically target the candidate (e.g., in recruitmentcommunications, etc.). In at least one embodiment, because candidatesmay not be conscious of all factors driving their own employmentdecisions, in the same exemplary scenario, the hybridity analysis mayinform the candidate of potential factors to consider when evaluatingfuture employment sources (e.g., is the offered hourly wage of aCCG-based position greater than 125% of the candidate's currenteffective hourly wage, does the CCG-based position offer remote work,etc.).

Exemplary Embodiments

Referring now to the figures, for the purposes of example andexplanation of the fundamental processes and components of the disclosedsystems and processes, reference is made to FIG. 1 , which illustratesan exemplary networked environment 100. As will be understood andappreciated, the exemplary, networked environment 100 shown in FIG. 1represents merely one approach or embodiment of the present system, andother aspects are used according to various embodiments of the presentsystem.

In various embodiments, the networked environment 100 includes aprediction system configured to perform one or more processes forpredictive targeting and engagement. The networked environment 100 mayinclude, but is not limited to, a computing environment 201, one or moredata sources 203, and one or more computing devices 205 over a network204. The network 204 includes, for example, the Internet, intranets,extranets, wide area networks (WANs), local area networks (LANs), wirednetworks, wireless networks, or other suitable networks, etc., or anycombination of two or more such networks. For example, such networks caninclude satellite networks, cable networks, Ethernet networks, and othertypes of networks.

According to one embodiment, the computing environment 201 includes, butis not limited to, a candidate service 207, a model service 209, acommunication service 211, and a data store 213. The elements of thecomputing environment 201 can be provided via a plurality of computingdevices that may be arranged, for example, in one or more server banksor computer banks or other arrangements. Such computing devices can belocated in a single installation or may be distributed among manydifferent geographical locations. For example, the computing environment201 can include a plurality of computing devices that together mayinclude a hosted computing resource, a grid computing resource, and/orany other distributed computing arrangement. In some cases, thecomputing environment 201 can correspond to an elastic computingresource where the allotted capacity of processing, network, storage, orother computing-related resources may vary over time.

In various embodiments, the data source 203 generally refers to internalor external systems, pages, databases, or other platforms from whichvarious data is received or collected. Non-limiting examples of datasources 203 include, but are not limited to, human resources systems,recruitment systems, resume processing systems, applicant and talentpools, public databases (e.g., criminal record systems, companyinformation databases, university systems, social media platforms, andetc.), private and/or permissioned databases, webpages, and financialsystems. In one example, a data source 203 includes a social networkingsite for professional development from which the computing environment201 collects and/or receives candidate profiles. In another example, adata source 203 includes a geolocation service form which the computingenvironment 201 retrieves addresses and other location data.

The candidate service 207 can be configured to request, retrieve, and/orprocess data from data sources 203. In one example, the candidateservice 207 is configured to automatically and periodically (e.g., every6 hours, 3 days, 2 weeks, etc.) collect position fulfillment informationfrom a database of a recruitment agency. In another example, thecandidate service 207 is configured to request and receive backgrounddata (e.g., criminal history, debt information, etc.) from a backgroundcheck service. In another example, the candidate service 207 isconfigured to receive credit reports from a credit monitoring platform(e.g., such as Equifax™, Transunion™, or Experian™).

The candidate service 207 can be configured to monitor for changes tovarious information at a data source 203. In one example, the candidateservice 207 monitors for changes to employment status for a plurality ofuser accounts at a social networking site. In this example, thecandidate service 207 detects that an employment status of a particularindividual has changed from “Software Development 3 at Company A” to“Freelance Coder.” Continuing this example, in response to thedetermination, the candidate service 207 automatically collects the newemployment information, which may be stored in the data store 213. Thecandidate service 207 can perform various data analysis, modifications,or normalizations to the various information. The candidate service 207can determine likely categories or bins for various data for eachcandidate. As an example, the candidate service 207 can determine thatthe “Software Development 3 at Company A” fits into a middle-level binfor job skill for the job title being a “3” of 5 level and is likely afull time position based on title and company information. Further, thecandidate service 207 can determine that the “Freelance Coder” positionmay be of the CCG category and that the skill level associated with theposition may be indeterminate.

In some embodiments, the candidate service 207 is configured to performone or more actions, for example, in response to input received from acomputing device 205. In one example, in response to a request forinformation on a particular candidate profile, the candidate service 207analyzes historical candidate data 217 and position data 219 anddetermines positions and position types that the particular candidateprofile viewed, selected, or dismissed within a particular time period(e.g., within the past 2 weeks, 3 months, 2 years, etc.). In thisexample, the positions and position types are displayed at a computingdevice 205 from which the request is received. In another example, thecandidate service 207 identifies and transmits candidate criteriademonstrated by individuals employed with particular employment sources(e.g., non-CCG sources, contract sources, etc.), with particularorganizations, and/or with particular job titles. In this example, thecandidate criteria can provide a candidate or a recruiter with anoverview of exemplary candidate qualities and other information that maybe relevant to recruitment processes for other employment sources,organizations, or positions (e.g., which may be similar or dissimilar tothose with which the candidate criteria is associated). In anotherexample, the candidate service 207 receives a request to evaluate aparticular candidate profile for a CCG position. In this example, thecandidate service 207 retrieves candidate data 217 (and/or other data)with which the particular candidate profile is associated and comparesthe candidate data 217 to historical candidate, position, or recruitmentdata with which the CCG position (or similar positions) are associated.Continuing the example, based on the comparison, the candidate service207 determines one or more deficiencies in qualifications, experience,etc. that, when filled, may increase the likelihood that the candidateis accepted to the CCG position. In the same example, the one or moredeficiencies are displayed on the computing device 205.

The model service 209 can be configured to perform various data analysisand modeling processes. In one example, the model service 209 generatesand trains machine learning models for predicting a likelihood of acandidate to be a CCG worker, to engage with a CCG-relatedcommunication, and/or to leave a current position for a CCG position. Inanother example, the model service 209 generates and trains machinelearning models for predicting a likelihood of communication language toelicit a response from a candidate (e.g., when a communication isgenerated based on and/or including the communication language). Themodel service 209 can be configured to generate, train, and executeneural networks, gradient boosting algorithms, mutual informationclassifiers, random forest classification, and other machine learningand related algorithms.

The model service 209 or candidate service 207 can be configured toperform various data processing and normalization techniques to generateinput data for machine learning and other analytical processes.Non-limiting examples of data processing techniques include, but are notlimited to, entity resolution, imputation, and missing, outlier, or nullvalue removal. In one example, the model service 209 performs entityresolution on candidate data for a plurality of individuals tostandardize terms such as position titles, company names, and locations.Entity resolution may generally include disambiguating manifestations ofreal-world entities in various records or mentions by linking andgrouping. In one embodiment, a dataset of candidate data may include aplurality of names for a single employer. In one or more embodiments,the system may perform entity resolution to identify data items thatrefer to the same employer, but may use variations of the employer'stitle. In an exemplary scenario, a dataset may include references to anemployer, Facebook, Inc.; however, various dataset entries may refer toFacebook, Inc. as Facebook™, Facebook, Inc., Facebook.com, and othervariants. In the same scenario, an embodiment of the system may performentity resolution to identify all dataset entries that include avariation of Facebook, Inc., and replace the identified dataset entrieswith the standard employer name Facebook, Inc. The model service 209 mayutilize historical data for various employers to rate a likely skilllevel of the candidate that worked at the employer. As an example, themodel service 209 may identify that future job positions for candidatesthat worked for Employer A correlate with better future job titles thancandidates that worked for Employer B. The model service 209 may adjustthe skill level or qualifications of a candidate based on evaluations ofother employees from a shared employer.

The communication service 211 can be configured to generatecommunications (e.g., or language to be included in communications). Insome embodiments, the communication service 211 performs one or morenatural language generation (NLG) processes to automatically generatelanguage for communications to candidates. The communication service 211can leverage machine learning processes (e.g., via the model service209) to generate communications that are optimized to increase alikelihood of the communications eliciting a response from one or morecandidates. In some embodiments, the communication service 211 optimizescommunications based at least in part on user data 215 with which arequest is associated. For example, the communication service 211 canretrieve, and include in a communication, contact details and a customsignature that corresponds to a user account from which a request wasreceived. In at least one embodiment, the communication service 211optimizes communications based on candidate data 217 with which aparticular candidate is associated and/or position data 219 orrecruitment data 221 with which a request is associated. For example,the communication service 211 may adjust a greeting, closing, writingstyle, or language style based on candidate data 217 for the particularcandidate.

The communication service 211 may evaluate writing style, grammar,vocabulary, or other aspects of candidate data 217 originating from eachparticular candidate, such as, for example, a resume, writing samples,email correspondence with recruiters, social media posts, and othercandidate data 217. In one example, the vocabulary may be adjusted tomatch a reading level of a particular candidate based on the candidatedata 217 indicating an education level or that a writing sample matchesa particular reading level. The communication service 211 may selectphrases or sayings that are included as authored by the candidate incandidate data 217. In at least one embodiment, the communicationservice 211 includes a third party natural language generation service.

The communication service 211 may evaluate historical information frompast communications to select language. As an example, the communicationservice 211 can determine that particular phrases may have a higherresponse rate than other phrases by analyzing past communications andknown outcomes of those past communications. In one example, a “I hopeyou are doing well” greeting to start an email may have a 5% higherresponse rate than starting the email with “We have a great jobopportunity for you.” The communication service 211 can select languageand style (e.g., fonts, font size, font color, use of bold, italics,underline, etc.) that is determined to correlate with an improvedresponse rate or an improved ultimate job placement rate.

The data store 213 can store various data that is accessible to thevarious elements of the computing environment 201. In some embodiments,data (or a subset of data) stored in the data store 213 is accessible tothe computing device 206 and one or more external system (e.g., on asecured and/or permissioned basis). Data stored at the data store 213can include, but is not limited to, user data 215, candidate data 217,position data 219, recruitment data 221, and model data 223. The datastore 213 can be representative of a plurality of data stores 112 as canbe appreciated.

The user data 215 can include information associated with one or moreuser accounts. For example, for a particular user account, the user data215 can include, but is not limited to, an identifier, user credentials,and settings and preferences for controlling the look, feel, andfunction of various processes discussed herein. User credentials caninclude, for example, a username and password, biometric information,such as a facial or fingerprint image, or cryptographic keys such aspublic/private keys. Settings can include, for example, communicationmode settings, alert settings, schedules for performing machine learningand/or communication generation processes, and settings for controllingwhich of a plurality of potential data sources 203 are leveraged toperform machine learning processes.

In one example, the settings include a configuration parameter for aparticular position location or region. In this example, when theconfiguration parameter is set to a particular region, a machinelearning and/or natural language generation process can be adjusted toaccount for a work culture or other set of factors with which theparticular region is associated. Various regions and sub-regions of theworld may demonstrate varying work cultures. Because work culture mayvary, data that is useful in generating accurate and precise CCG-relatedpredictions may also vary, in addition to variances in magnitudes ofimpact and impact directionality imposed on machine-learned predictions.For example, work culture of a particular region may be such thatemployees do not often experience mobility within their company. In thesame example, the work culture may be such that employees typicallyremain with their company for an extended time period (e.g., decades, ascompared to years in other work cultures). In various embodiments, thesystem may configure one or more machine learning and/or NLG processesto account for variations in work culture. For example, the system mayalter one or more machine learning parameter weights to reduce an impactor change impact directionality on likelihood predictions. In the aboveexample, the system may reduce machine learning parameter weights and/ormodify parameter impact directionality for parameters including numberof promotions, job latency, and job tenure, thereby reducing theparameters' impact on subsequently generated likelihood predictions.

The candidate data 217 can include, but is not limited to, candidatenames, locations, such as, for example, a list of current and previousaddresses, education history, job satisfaction (e.g., job and/orworkplace reviews), age, family status, marital status, debtobligations, financial health (for example, a credit score), and socialmedia activities (e.g., such as a list of followers, postings, etc.). Inone example, candidate data includes work history, such as past andcurrent job titles, positions, roles, employers, salary and/or wageinformation, candidate performance reviews, job locations, and resumes.In at least one embodiment, personally identifying data, financial data,social media data, and other personal data (e.g., family and maritalstatus, etc.) may not be collected or leveraged or may be intentionallyexcluded for processes described herein (e.g., in accordance with legalpolicy, corporate policy, data privacy policy, user consent parameters,etc.). In some embodiments, candidate data 217 includes criminalrecords, degree history, liens, voting history, and other data obtainedfrom investigative processes (e.g., such as information obtained from abackground check performed on a particular candidate). The candidatedata 217 can include assets owned by candidates including timinginformation as to when those assets were purchased, such as, forexample, real estate including primary residences and secondaryresidences, vehicles, boats, planes, and other assets. The candidatedata 217 can include current estimated values and debts associated witheach asset.

The position data 219 can refer to data associated with employmentopportunity and fulfillment information. Position data 219 can include,but is not limited to, position titles, position duties,responsibilities, and tasks. Position data 219 may include positionlocations, such as, for example, a list of current and previousaddresses to which candidates holding a position have been located.Position data 219 may include position fulfillment history, such as, forexample, past and current position holders, position providers (e.g.,institutions, companies, etc. that offer or provide labor fillingCCG-based positions), salary and/or wage information, position reviews,position provider reviews, and resumes, C.V.'s, or the like, of past andcurrent position holders. Position data 219 may include past and currentposition holder education histories, job satisfaction (for example, joband/or workplace reviews related to any number of current or past-heldpositions), age, family status(es), marital status(es), past and currentdebt obligations, past and current financial health, (for example, acredit score), and social media activities. In some embodiments, thenetworked environment 100 is configured to process a position holder'sresume and/or employee files and determine various position data 219,such as a work history, education history, and location history.

The recruitment data 221 can refer to data associated with an employmentopportunity, such as a desired set of experiences or other criteria. Inone example, the recruitment data 221 includes candidate criteria, suchas desired experience (e.g., skills and/or work history), location,education, compensation history and/or requirements, and other candidatequalifications.

The model data 223 can include data associated with machine learning andother modeling processes described herein. Non-limiting examples ofmodel data 223 include, but are not limited to, machine learning models,parameters, weight values, input and output datasets, training datasets,validation sets, configuration properties, and other settings. In oneexample, model data 223 includes a training dataset including historicalcandidate data 217, recruitment data 221, and position data 219. In thisexample, the training dataset can be used for training a machinelearning model to predict a likelihood of a candidate being willing toconsider or accept a CCG job position.

In various embodiments, the model data 223 may include work culturecategories that can be provided as an input to machine learningprocesses. In at least one embodiment, a work culture category may beused by the modeling service 209 to modify data that is input to andanalyzed via one or more machine learning models. In one embodiment, awork culture category may be used by the modeling service 209 to modifyoutputs generated by one or more machine learning models. For example, awork culture category associated with a work culture that utilizes aSunday-Thursday work week may cause a machine learning model todowngrade or reduce generated likelihoods of a candidate (operating inthat work culture) accepting a gig-based position that includes Fridayworking hours.

In one or more embodiments, a work culture category may be used by themodeling service 209 to cause one or more machine learning models toinitialize parameter weights at a higher or lower magnitude, or with apositive or negative directionality. For example, a work culturecategory for a “Country X” may be input to a machine learning processfor identifying candidates likely to accept a gig-based position. In thesame example, the “Country X” work culture category may cause one ormore machine learning models to exclude input data related to jobtenure, promotions, and employer reviews when analyzing candidates from“Country X” (e.g., establishing that job tenure, promotions, andemployer reviews are not predictive for likelihood of the candidates toaccept a gig-based position). Continuing with this example, the “CountryX” work culture category may also cause the one or more machine learningmodels to establish a negative impact directionality on parameters anddata related to experience level and skill levels (e.g., establishingthat a greater experience level and skill level makes candidates from“Country X” less likely to accept gig-based positions).

In an alternate example, a work culture category for a “Country Y” canbe input to the machine learning process for identifying candidateslikely to accept a gig-based position. In the same example, the one ormore machine learning models may exclude data related to location andage when analyzing candidates from “Country Y” (e.g., establishing thatlocation and age are not predictive for likelihood of the candidates toaccept a gig-based position). Continuing the same example, the “CountryY” work culture category may cause the one or more machine learningmodels to increase an initial weight of parameters related to joblatency (e.g., establishing that job latency may be more predictive forlikelihood to accept a gig-based position).

The computing device 205 can be any network-capable device including,but not limited to, smartphones, computers, tablets, smart accessories,such as a smart watch, key fobs, and other external devices. Thecomputing device 205 can include a processor and memory. The computingdevice 205 can include a display 225 on which various user interfacescan be rendered by a candidate application 229 to configure, monitor,and control various functions of the networked environment 100. Thecandidate application 229 can correspond to a web browser and a webpage, a mobile app, a native application, a service, or other softwarethat can be executed on the computing device 205. The candidateapplication 229 can display information associated with processes of thenetworked environment 100 and/or data stored thereby. In one example,the candidate application 229 displays candidate profiles that aregenerated or retrieved from user data 215. In another example, thecandidate application 229 displays a ranked list of candidatesclassified as “Most Likely to be CCG” or, in another example, as “MostLikely to Engage with CCG Communications.”

The computing device 205 can include an input device 227 for providinginputs, such as requests and commands, to the computing device 205. Theinput devices 227 can include a keyboard, mouse, pointer, touch screen,speaker for voice commands, camera or light sensing device to reachmotions or gestures, or other input device. The candidate application229 can process the inputs and transmit commands, requests, or responsesto the computing environment 201 or one or more data sources 203.According to some embodiments, functionality of the candidateapplication is determined based on a particular user account or otheruser data 215 with which the computing device 205 is associated. In oneexample, a first computing device 205 is associated with a recruitmentuser account and the candidate application 229 is configured to displaycandidate profiles and provide access to candidate prediction andcommunication generation processes. In this example, a second computingdevice 205 is associated with a candidate user account and the candidateapplication 229 is configured to allow the computing device 205 totransmit candidate data 217 to the computing environment 201 and todisplay communications, such as recruitment messages and alerts.

FIG. 2 shows an exemplary prediction process 200, according to oneembodiment of the present disclosure. As will be understood by onehaving ordinary skill in the art, the steps and processes shown in FIG.2 (and those of all other flowcharts and sequence diagrams shown anddescribed herein) may operate concurrently and continuously, aregenerally asynchronous and independent, and are not necessarilyperformed in the order shown.

At step 248, the process 200 includes receiving a request. The requestcan be to initiate predictive targeting processes for particularcandidate data 217, position data 219, or recruitment data 221. Forexample, the request can be to initiate predictive targeting processesfor identifying candidates that are likely to be CCG workers, to accepta particular CCG-based position or role, or to engage with a particularCCG-related communication. As another example, the request can be toinitiate a talent retention analysis for a particular set of candidatesthat are employed at a particular company. In this example, the talentretention analysis can leverage machine learning processes to predictwhich of the set of candidates is more likely to leave non-CCG role andaccept a CCG position.

In at least one embodiment, the request includes a description of one ormore candidates, one or more positions, and/or one or more positionoffers. The description can include, for example, candidate criteriathat provide a set of candidate parameters for a particular position(e.g., the particular position corresponding to a CCG class ofpositions). The request can be received from a computing device 205(e.g., via inputs to a candidate application 229). The request can beautomatically initiated, for example, in response to detecting a changein the status of one or more candidates, one or more positions, or otherinformation. The request can be automatically initiated based on apredetermined schedule. For example, a particular user account caninclude a setting to automatically perform predictive targeting every 24hours, every week, every quarter, etc. The request can include an outputformat, such as, for example, a ranked list of candidates, a report ofmost and/or least influential parameters, or a summary of a mosthighly-ranked candidate (e.g., including information associated with thecandidate, one or more predictions, and one or more influentialparameters).

At step 251, the process 200 includes receiving data. The particulardata that is received can be based at least in part on the request. Inone example, the request includes a command to initiate machine learningprocesses to predict a respective likelihood of interest in a CCG classof positions for each of the one or more candidates (e.g., the requestincluding a description thereof each). In this example, in response tothe request, the candidate service 207 receives candidate data 217,position data 219, and/or recruitment data 221 with which a first and asecond subset of individuals are associated. Continuing the example, thefirst subset of individuals can correspond to a known non-CCG workerclass and the second subset of individuals can correspond to a known CCGworker class.

Receiving the data can include collecting and/or retrieving data fromone or more data sources 203 or the data store 213. For example, thecandidate service 207 automatically requests and receives historicalrecruitment information and position fulfillment information from anexternal human resources system. In another example, the candidateservice 207 automatically scrapes and indexes publicly accessible datasources 203 to collect candidate data 217, position data 219,recruitment data 221, and/or other information. In another example, thecandidate service 207 automatically identifies and retrieves jobpostings and/or historical recruitment data 221. In this example, thecandidate service 207 identifies the job postings based on criteriaincluded in a request, such as, for example, a desired experience level,skill set, location, and employment history. The computing environment201 can be configured to receive data for a particular number (e.g.,100, 1000, 10,000, etc.) and/or class of candidates. In one example, thecandidate service 207 retrieves historical candidate data 217 describing10,000 known CCG-based position holders and 10,000 known non-CCGposition holders. In another example, historical candidate data 217 isretrieved that describes known CCG-related communication engagers andknown CCG-related communication non-engagers, or that describes knownCCG-based position acceptors and known CCG-based position rejecters.

According to one embodiment, a number of candidates, or the like,analyzed in a machine learning process is automatically determined(e.g., based on data available or based on other factors or processes).In some embodiments, the number of candidates is manually configured(e.g., via input). In some embodiments, data is requested or receivedvia an application programming interface (API) that provides access toone or more data sources 203. In at least one embodiment, the presentsystems and methods may omit personally identifying or other personaldata from collection and/or from processes described herein or, in someembodiments, require an affirmative consent input from a candidateassociated with the personally identifying and other personal data. Inone or more embodiments, a consent input may refer to a selection madeon a computing device 205 in response to a consent query that askswhether or not a candidate consents to usage of particular personallyidentifying and/or other personal data (e.g., with an indication beingprovided and relating that anonymization of such data will be performedif the candidate consents to its use).

At step 254, the process 200 includes processing data. Data processingcan include, but is not limited to, performing text recognition andextraction techniques, data normalization techniques (e.g., such as dataimputation or null value removal), entity resolution techniques, and/or(pseudo-)anonymization techniques. In at least one embodiment,processing the data includes anonymizing or pseudo-anonymizingpersonally-identifying information (PII). In one example, the candidateservice 207 automatically process candidate resumes, candidateinformation, and other historical candidate data stored in one or moredatabases. In this example, the model service 209 receives the processescandidate resumes, information, and historical data, and performs one ormore entity resolution techniques to identify and standardize equivalentinformation across the processed data (e.g., by replacing mutual termswith a standardized term). In another example, the candidate service 207processes candidate information scraped from a public profile hosted ata social media webpage. In this example, the candidate service 207 canrecognize key information and terms, such as, for example, a currentemployment status, a particular skill or experience, a particularqualification, or other factors.

In one example, the candidate service 207 processes a candidate's resumeand identifies a work history, education history, and location history.In the same example, the candidate service 207 applies apseudo-anonymization technique to the location history to removeidentifying details (e.g., house number, street name, etc.) from homeaddresses included in the location history. In the same example, themodel service 209 performs an entity resolution process to replacetitles and roles in the work history with industry-standardizedpositions.

A machine learning process (e.g., such as an embodiment of the machinelearning process 300 shown in FIG. 3 ) can be performed to generate andtrain one or more machine learning models. The machine learning modelcan be configured to generate various predictions, such as, for example,predicting a likelihood of a candidate to be a CCG worker, to engagewith a CCG-related communication, or to leave a non-CCG position for aCCG position. By the machine learning process, a first machine learningmodel can be trained using a training dataset including known inputs andoutputs. The first machine learning model can include one or moreparameters and one or more weight values that determine a magnitude ofinfluence each parameter contributes to an output of the first machinelearning model. The parameters and/or weights can be analyzed todetermine an accuracy of the first machine learning model. Based on theanalysis, the parameters and/or weights can be optimized (e.g., toimprove accuracy metrics, reduce error metrics, or improve othermetrics, such as bias metrics). One or more secondary machine learningmodels can be generated based on the optimized parameters and/or weightvalues, the secondary machine learning model being configured togenerate output from input for which there is not known output.

A first machine learning model can be leveraged to identify trends,correlations, and patterns between candidate descriptions andcorresponding candidate statuses (e.g., such as holding a CCG positionor having not engaged with previous CCG-related recruitmentcommunications). In one example, a first machine learning model istrained to classify candidates as a CCG-based position acceptor or aCCG-based position rejecter. In this example, the first machine learningmodel is trained using a dataset of candidate descriptions associatedwith 10,000 known CCG-based position acceptors and 10,000 knownCCG-based position rejecters. Continuing this example, a particulariteration of the first machine learning model is determined to satisfyone or more error metrics and output of the particular generation (e.g.,scores, weight values, parameter to output relationships) is leveragedto configure parameters and/or other properties of a secondary machinelearning model for predicting a likelihood that a candidate will accepta CCG-based position. In the same example, the output indicates thatnegatively predictive factors include holding a non-CCG position formore than five years, receiving a promotion at a non-CCG position withinthe past two years, and residing at the same location for at least fouryears. Continuing the example, for the secondary machine learning model,the negatively predictive factors (e.g., or equivalent machine learningparameters) are assigned a negative directionality which causesdemonstration of one or more negatively predictive factors by acandidate to contribute negatively to a prediction score. In the sameexample, a magnitude of the negative directionality is based at least inpart on weight values with each factor is associated and which weregenerated during training of the first machine learning model. Eachweight value can determine, at least in part, a level of influence thecorresponding factor exercises over an output of the secondary machinelearning model.

At step 257, the process 200 includes generating output. In one or moreembodiments, upon satisfying error metric thresholds, a trained machinelearning model may be used to analyze candidate and/or position datadescribing candidates whose status for a particular classification isunknown. In at least one embodiment, the system may execute the trainedmachine learning model on an input dataset, and the trained machinelearning model may output a set of likelihood predictions (e.g.,Booleans, scores, etc.) describing, for each candidate, a likelihood ofbeing associated with a predetermined classification, status, orcategory. For example, the system executes a trained, secondary machinelearning model to predict, for each respective candidate, a likelihoodbeing a CCG-based position holder, a likelihood of accepting a CCG-basedposition, or a likelihood of engaging with CCG-related communications.In this example, the input dataset includes one or more of candidatedata 217, position data 219, and recruitment data 221 (e.g., that wascollected or received at step 251 and processed at step 254).

In at least one embodiment, the machine learning model may also outputor identify, for each candidate, one or more portions or parameters ofthe input dataset that were most influential upon the candidate'sassociated likelihood prediction. In one or more embodiments, toidentify and report the most influential portions, the machine learningmodel may determine one or more machine learning parameters that weremost heavily weighted. In at least one embodiment, the machine learningmodel determines and outputs one or more most-weighted machine learningparameters that positively influenced a likelihood prediction, and mayalso identify one or more most-weighted machine learning parameters thatnegatively influenced a likelihood prediction. In one example, for acandidate classified as more likely to be a CCG-based position acceptor,parameter weight values of an associated machine learning model indicatethat the candidate's resume, including a mention of prior CCG-based workexperience, is the most positively impacting parameter, and indicatethat the candidate's current above-industry-average non-CCG salary isthe most negatively impacting parameter. By identifying and reportingmost-weighted parameters, the computing environment 201, in variousembodiments, may provide for identification and tracking of parametersand candidate factors that are most important in evaluating CCG status,CCG-based position acceptance, or CCG-related communication engagement.

In an exemplary scenario, a threshold-satisfying secondary machinelearning model is retrieved or generated (e.g., based on correspondingmodel data 223). The secondary machine learning model is configured toanalyze and generate CCG position holder predictions for 1,000candidates of unknown CCG-based position status. Descriptions for eachof the 1,000 candidates are received and processed to generate an inputdataset. The particular iteration of the secondary machine learningmodel is executed on the input dataset and generates a prediction outputincluding a plurality of prediction scores by which each of the 1,000candidates is classified. The prediction output further includes one ormore parameters that were most negatively or positively impactful oneach candidate's associated prediction score.

In various embodiments, machine learning processes may be performed invarious configurations, workflows, and sequences, for example, to filterthrough candidates in a particular manner or order. In one example, afirst machine learning process is performed to predict candidates likelyto engage with any recruitment-related communication, and a secondmachine learning process to predict candidates, from a subset ofcandidates identified in the first process, that are likely to engagewith CCG-related recruitment communications. In another example, a firstmachine learning process is performed to predict, from a population ofknown non-CCG-based position holding candidates, a first set ofcandidates that are most likely to be interested in CCG-relatedpositions. In the same example, a second machine learning process isperformed to predict, from first the set of candidates, a subset ofcandidates that are most likely to engage with CCG-related recruitmentcommunications.

At step 260, the process 200 includes generating one or moreclassifications. The classification can be generated by comparing outputof a machine learning model to one or more thresholds (e.g., forexample, thresholds defined similarly to those provided in Equation 2).In one example, a machine learning model is configured to predict alikelihood of a candidate being a CCG position holder. In this example,the machine learning model generates a prediction score that is comparedto one or more thresholds. The one or more thresholds can include, forexample, a first score level that corresponds to a “Less Likely to beCCG” classification, a second score level, (e.g., greater than the firstscore level) that corresponds to a “Likely to be CCG” classification,and a third score level that corresponds to a “More Likely to be CCG”classification. The thresholds for generating classifications can beheuristically determined and/or computationally optimized. In oneexample, the thresholds are optimized by performing one or more K-foldscross validation processes using machine learning models trained ontraining datasets of varying composition (e.g., and, in some instances,mutually exclusive compositions).

In one example, first and secondary machine learning process areperformed to generate a secondary machine learning model configured topredict a likelihood of a candidate being a CCG position holder. In thisexample, from an input set of candidate descriptions, the secondarymachine learning model generates output including a prediction scorecorresponding to each of 1,000 candidates. Continuing the example, thecandidate service 207 compares each prediction score to a plurality ofincreasing thresholds including a first value range corresponding to a“Least Likely to be CCG” classification, a second value rangecorresponding to a “Less Likely to be CCG” classification, a third valuerange corresponding to a “Likely to be CCG” classification, a fourthvalue range corresponding to a “More Likely to be CCG” classification,and a fifth value range corresponding to a “Most Likely to be CCG”classification. In the same example, the candidate service classifies 50candidates as most likely to be a CCG-based position holder, classifying100 candidates as more likely to be a CCG-based position holder,classifying 100 candidates as likely to be a CCG-based position holder,classifying 250 candidates as less likely to be a CCG-based positionholder, and classifying 500 candidates as least likely to be a CCG-basedposition holder.

At step 263, the process 300 includes performing one or more appropriateactions. The actions can be performed, for example, based on one or morepreferences or settings included in a request or stored in user data 215with which the request is associated. Non-limiting examples of actionsinclude, but are not limited to, generating particular language designedto provoke a response from one or more candidates, generating a rankingof candidates, altering a profile with which a candidate is associated(e.g., to indicate a status as a CCG worker or to enable or disableparticular functions, such as communicating with particular recruiters),determining one or more parameters that most contributed to aclassification of a candidate, excluding a candidate from furtherrecruitment processes, generating and rendering a graphical userinterface (e.g., via a candidate application 229), generating a portalfor accessing machine learning outputs and classifications, generating acandidate- or organization-level summary of outputs, and initiating aprocess to monitor one or more data sources 203 for changes to dataassociated with a candidate.

In one or more embodiments, for each candidate, output of the trainedsecondary machine model includes one or more parameters that were mostnegatively or positively impactful on the candidate's associatedprediction and, thus, on the candidate's classification. In one example,for a candidate classified as more likely to be a CCG-based positionholder, the associated output includes parameters and/or weight valuesindicating that that the candidate's work history, including employmentat known CCG labor suppliers, was the most positively impactingparameter, and indicating that the candidate's current five-year tenurein a non-CCG position was the most negatively impacting parameter.

In another example, the process 200 is initiated in response to arequest from a particular user account. In this example, the particularuser account includes an alert setting for alerting the particular userwhen a candidate is classified as “More Likely to be CCG.” Continuingthis example, classifications are generated for a plurality ofcandidates and a subset of candidates are classified as “More Likely tobe CCG.” In the same example, an alert is generated and transmitted tothe particular user account, the alert including an identification ofthe subset of candidates.

A communication process (e.g., such as an embodiment of thecommunication process 500 shown in FIG. 5 ) can be initiated to generateone or more communications. For example, a communication process can beinitiated to generate an email by executing one or more natural languagegeneration processes. One or more machine learning models can begenerated and trained to predict optimal language for the communication.The machine learning model can be configured to analyze potentialcommunication language and data with which a candidate is associated.The machine learning model can be trained to predict a likelihood of thecandidate engaging with a communication including the potentialcommunication language.

In various embodiments, the communication service 211 generates one ormore visualizations describing individual candidates, group ofcandidates, organizations, and/or other subjects. In at least oneembodiment, the communication service 211 is configured to generatevisualizations such as radar charts, meters, and other representationsof machine-learned predictions, candidate or organization performances,candidate or organization criteria, or other data. In at least oneembodiment, the computing environment 201 is configured to generateand/or service one or more online portals, web pages, GUI's, or otheronline media that allow users to view, configure, and interact withprocesses, including inputs and outputs thereof, described herein. Invarious embodiments, the system is configured to generate a portal(e.g., a webpage, application, etc.) that allows a user to searchthrough and view system profiles of candidates evaluated as describedherein. In one or more embodiments, the portal includes a searchfunction that allows the system to receive and process search criteria,and return one or more results (e.g., such as system profiles). In oneexample, the computing environment 201 receives candidate criteria for acandidate that possesses at least five years of software engineeringexperience and, based on machine learning analysis, is predicted to bemore likely to accept a contingent, contract, and/or gig (CCG) class ofposition for their next employment source. In the same example, thecomputing environment 201 processes the candidate criteria and serves,via the portal, a list of candidates (e.g., or candidate profiles) thatsatisfy the candidate criteria.

In at least one embodiment, candidate or organization-level summaries ofoutput are generated. A summary can include, for example, candidateparameters, candidate descriptions, and/or ranked candidate lists (e.g.,generated based on assigned classifications). In at least oneembodiment, an organization-level labor summary is generated byanalyzing an organization's labor force via the machine learningtechniques described herein to identify one or more members or groupswithin the labor force that are classified as a particular status (e.g.,likely to accept a CCG-based position, likely to engage in CCG-relatedcommunications, etc.). In one example, the computing environment 201receives data describing each employee of an organization, and predictseach employee's likelihood of accepting a CCG-based position and/orengaging with CCG-related recruitment notifications. In variousembodiments, the computing environment 201 may determine, and include inthe summary, one or more parameters that are most negatively or mostpositively influencing an employee's predicted classification. Forexample, the model service 209 can determine one or more factors thatmost positively and negatively contribute to an employee's predictedclassification as someone more likely to leave the organization forCCG-based roles and/or to engage with CCG-based recruitmentcommunications. In at least one embodiment, by identifying factors thatmay potentially drive employees to leave or consider leaving theorganization, the system may provide the organization with indicationsincluding, but not limited to, factors that may be changed to reduce alikelihood of an employee departing or considering departure from theorganization, employees that may need to be replaced in the near future,sub-units within the organization (e.g., departments, groups, etc.) thatdemonstrate labor retention risks.

FIG. 3 shows an exemplary machine learning process 300, according to oneembodiment of the present disclosure. In various embodiments, machinelearning models are configured to analyze one or more descriptions ofone or more candidates and, based on the analysis, predict a respectiveclassification, interest, or other output with which the based on theone or more candidates are associated. In at least one embodiment,analysis of a candidate includes analyzing various input data (andparameters derived therefrom) including, but not limited to, resumes,online profiles (e.g., such as LinkedIn™ pages, Indeed™ pages, GitHub™profiles, and other online profiles and accounts), education history,employment history, current location and/or location history,compensation data (e.g., such as salary, hourly wage, etc.), employerdata (e.g., such as employer size, employer location, corporatestructure, job latency, hiring patterns, past and current job listings,reviews, and executive profiles), financial data (e.g., such as creditscores and debt obligations), marital status, family status, age,experience level, skills and certifications, criminal history, andaffiliation (e.g., such as affiliations with particular organizations,vendors, labor suppliers, persons, etc.).

At step 303, the process 300 includes generating one or more trainingdatasets. In at least one embodiment, to identify candidates that mayhold or may have held CCG-based positions, a first training dataset isgenerated that includes, but is not limited to, a first portionincluding candidate and/or position data describing known CCG-basedposition holders, and a second portion including candidate and/orposition data describing known non-CCG position holders. According toone embodiment, a training dataset or dataset may generally refer to aset of historical data that is evaluated by a machine learning model.The machine learning model can evaluate the training dataset for thepurposes of improving model accuracy, reducing error, or otherwiseimproving the model. A training dataset (also referred to as a“teaching” dataset) can include labeled or unlabeled data (e.g., thelabeled data including a known output with which the data isassociated). In one example, to identify candidates that are likely toaccept a CCG-based position for their next employment source, a firsttraining dataset and second training dataset are generated that includesa first portion including candidate and/or position data describingknown CCG-based position acceptors, and a second portion is generatedthat including candidate and/or position data describing known CCG-basedposition rejecters.

In at least one embodiment, to identify candidates that are likely toengage with CCG-related recruitment communications, a first trainingdataset is generated that includes a first portion including candidateand/or position data describing individuals who are known CCG-relatedcommunication engagers and a second portion including candidate and/orposition data describing individuals who are known CCG-relatedcommunication non-engagers.

In some embodiments, generating the training dataset includes generatingor retrieving one or more datasets describing known suppliers of CCGlabor and/or known CCG-based positions. In this example, the candidateservice 207 evaluates candidate data 217, position data 219, and otherdata to determine if a candidate has been or is affiliated with one ormore known suppliers of CCG labor and to determine if a candidate hasheld or currently holds a known CCG-based position. The candidateservice 207 can analyze a candidate's resume to determine if names ofknown CCG labor suppliers are included or to determine if titles ofknown CCG-based positions are included. Continuing the example, inresponse to identifying known CCG-based positions and/or one or moreknown CCG labor suppliers, the model service 209 includes thedeterminations (e.g., and/or data that contributed to thedeterminations) as parameters of a training dataset for predicting alikelihood of a candidate being a CCG-based position holder, beinglikely to accept a CCG-based position, and/or being likely to engagewith CCG-related communications.

At step 306, the process 300 includes configuring one or more parametersand generating a machine learning model (e.g., based on the one or moreparameters). Configuring the one or more parameters can includeadjusting one or more parameters to reduce an error metric, increase anerror metric, or improve other output-related metrics. In at least oneembodiment, to reduce the error metrics, the system may perform actionsincluding, but not limited to, identifying one or more most-erroneousparameters that most heavily contributed to error metrics, excluding oneor more identified most-erroneous parameters from further machinelearning processes, increasing and/or decreasing various parameterweights such that identified most-erroneous parameters may contributeless to the one or more error metrics may be reduce, and executing oneor more loss function optimization algorithms.

The system can configure the one or more parameters by adjusting one ormore weight values based on output of another machine learning model. Inan exemplary scenario, a first machine learning process is performed totrain one or more machine learning models to classify a candidate as aCCG-based or non-CCG-based position holder. By the first machinelearning process, differences, correlations, and data patterns betweenCCG position holders and non-CCG position holders are determined andleveraged to generate a secondary machine learning model to predict alikelihood of a candidate being a CCG-based position holder.

Continuing the exemplary scenario, a training dataset is generated basedon sets of historical candidate data 217 (and/or other data) with whichknown CCG position holders and non-CCG position holders are associated,respectively. The training dataset includes data describing 10,000 knownCCG-based position holders and 10,000 known non-CCG position holders.Using the training dataset, the first machine learning model is trainedto predict a classification of candidates as CCG position holding ornon-CCG position holding. The first trained machine learning model isconfigured to assign initial weights and/or directionality to variousparameters (e.g., candidate data, and other information) that aregenerated from the training dataset (or other input data). From thetraining process, the model service 209 determines that previouslyholding a non-CCG position for a tenure greater than two years isnegatively predictive for likelihood of a candidate to be a CCG-basedposition holder. Based on the determination, the model service 209assigns a negative directionality to the identified parameter, therebyindicating that a candidate's demonstration of the identified parametershould cause a machine learning model to decrease the candidate'spredicted likelihood of being a CCG-based position holder.

In the same scenario, the model service 209 determines thatdemonstrating a job latency of at least six months is positivelypredictive for likelihood of a candidate to be a CCG-based positionholder. Based on the determination, the model service 209 assigns apositive directionality to the identified parameter, thereby indicatingthat a candidate's demonstration of the identified parameter shouldcause the first trained machine learning model to increase thecandidate's predicted likelihood of being a CCG-based position holder.The magnitude of each directionality is determined based on the weightvalue with which the parameter is associated and which was generated bythe trained first machine learning model.

Continuing the exemplary scenario of the preceding paragraphs, a secondmachine learning process is performed to generate one or more secondarymachine learning models based on the first trained machine learningmodel. The secondary machine learning model is trained to predict one ormore likelihoods of each of the 10,000 known CCG-based position holdersand each of the 10,000 known non-CCG position holders as being aCCG-based position holder. The secondary machine learning model can betrained using on the determinations of the first machine learning model,such that determined trends, weights, directionalities, and otherparameter-influencing factors are leveraged to improve the performanceof the secondary machine learning model.

At step 309, the process 300 includes training a machine learning model(e.g., using one or more training datasets). Training the machinelearning model can include, but is not limited to, executing the machinelearning model on an input (e.g., a training dataset or subset thereof)and generating an output, such as a prediction score or classification.In one or more embodiments, the model service 209 generates and trains,using the first training dataset, one or more primary machine learningmodels to identify differences between position holders of a knownCCG-based first dataset portion and position holders of a known non-CCGsecond dataset portion. In at least one embodiment, by identifying thedifferences, the one or more primary machine learning models may betrained to identify candidate criteria (e.g., candidate data, etc.) thatare predictive for CCG-based position holders. According to oneembodiment, one or more subsequent machine learning models may becreated from the one or more primary machine learning models, and may beconfigured to analyze candidates and predict a likelihood that one ormore candidates are CCG-based position holders. In at least oneembodiment, the model service 209 generates and trains one or moreprimary machine learning models to identify differences between positionholders of a first CCG-acceptor dataset portion and position holders ofa second CCG-rejecter dataset portion.

In one or more embodiments, the model service 209 may generate andtrain, using a first training dataset, one or more first machinelearning models to identify respective differences between individualsof a communication responder dataset and a communication non-responderdataset. In at least one embodiment, by identifying the differences, theone or more first machine learning models may be trained to identifycandidate criteria (e.g., candidate data, etc.) that, for example, arepredictive for CCG-related communication engagement or for CCG-basedposition acceptance. In various embodiments, one or more subsequentmachine learning models may be created from the one or more firstmachine learning models, and may be configured to analyze candidates andgenerate predictions therefor.

One or more secondary training datasets can be generated, for example,to support unsupervised (e.g., unlabeled) training or supervised (e.g.,labeled) training. In at least one embodiment, the model service 209generates a secondary training dataset that includes, for example,candidate and/or position data describing both known CCG-based and knownnon-CCG position holders. In various embodiments, the model service maygenerate a secondary training dataset including candidate and/orposition data describing both known CCG-based position acceptors andknown CCG-based position rejecters. In at least one embodiment, thesecond training dataset may be unlabeled (e.g., absent information thatidentifies position holders therein as CCG or non-CCG, or as a CCG-basedposition acceptor or rejecter). In one or more embodiments, one or moresecondary machine learning models may be trained to predict, from thesecond training dataset, position holders that may be CCG-based.According to one embodiment, the one or more secondary machine learningmodels may generate a first set of predicted CCG-based position holders.In one or more embodiments, the one or more secondary machine learningmodels may be trained to predict, from the second training dataset,individuals that may be CCG-based position acceptors.

At step 312, the process 300 includes analyzing output from one or moretraining models. Analyzing the output can include, but is not limitedto, comparing the output to an expected output and, based on thecomparison, computing one or more accuracy or error metrics (alsoreferred to as loss functions). In at least one embodiment, the modelservice 209 calculates one or more error metrics betweenmachine-predicted output and corresponding known output (e.g., from thefirst training dataset). In various embodiments, and may reconfigure ormodify the one or more secondary machine learning models to reduce theerror metrics (e.g., thereby increasing accuracy and/or precision ofsubsequently generated predictions).

In at least one embodiment, the system may compare the first set ofpredicted CCG-based position holders to the known CCG-based positionholder of the first training dataset and calculate one or more errormetrics quantifying the comparison, thereby determining how accuratelyand precisely the one or more secondary machine learning modelsidentified the CCG-based position holders. In at least one embodiment,the system may compare the first set of predicted CCG-based positionacceptors to the known CCG-based position acceptors of the firsttraining dataset and calculate one or more error metrics quantifying thecomparison, thereby determining how accurately and precisely the one ormore secondary machine learning models identified the CCG-based positionacceptors.

At step 315, the process 300 includes determining that a predeterminedthreshold, such as an error or accuracy threshold, is met. In variousembodiments, the system may repeat and iterate upon any trainingactivities until one or more dynamic and/or predetermined error metricthresholds are met. In response to determining that the predeterminedthreshold is met, the process 300 can proceed to step 318. In responseto determining that the predetermined threshold is not met, the process400 can proceed to step

At step 318, the process 300 includes storing the threshold-satisfyingmachine learning model. The machine learning model can be stored, forexample, as model data 223, including, but not limited to, trainingdatasets, error metrics, parameters, weight values, directionalityassignments, and configuration settings.

FIG. 4 shows an exemplary communication process 400, according to oneembodiment of the present disclosure. In various embodiments, thecommunication process 400 is performed to generate, based onmachine-learned results, strings of text for electronic communicationswith which a particular candidate (or set of candidates) is likely toengage (e.g., based on one or more aspects, analyses, and/orclassifications of the particular candidate). At step 403, the process400 includes receiving a request to initiate a communication. Therequest can be generated automatically or in response to an input. Forexample, the request is automatically initiated in response toperformance of a prediction process or a machine learning process. Inanother example, the request is automatically initiated based on apredetermined schedule for contacting candidates, the predeterminedschedule being retrieved from user data 215. The request can includeidentifications of one or more candidates for which communication isrequested, such as, for example, profile names or other identifiers.

At step 406, the process 400 includes generating one or more datasets,such as, for example input datasets and training datasets. The datasetcan be generated based at least in part on the request. For example,based on a particular candidate description or other factor provided inthe request, historical communication language and associated outcomescan be retrieved and used to generate a training dataset. In the sameexample, based on a candidate description and classification, a corpusof communication language is generated via a natural language generationprocess and is used to generate an input dataset. In this example, thetraining dataset can be used to train a machine learning model forclassifying communication language as more or less likely to elicitengagement and, based on the trained machine learning model, a secondmachine learning model can be generated to predict a likelihood of thecorpus of communication (e.g., or a subset thereof) eliciting engagementwhen included in a communication to the candidate with which thecandidate description is associated.

Generating the dataset can include retrieving, from one or moredatabases, recruitment language, candidate classifications, weightvalues, and/or parameters (e.g., candidate characteristics, metrics,etc.) with which the weight values are associated. According to oneembodiment, recruitment language includes, but is not limited to,communication templates, subject lines, introductions, body paragraphsor sentences, and key terms. In at least one embodiment, the retrievedparameters can be parameters determined to be most positively ornegatively influential for generating a classification of a candidate(e.g., such as a classification of a candidate as individual likely tobe interested in a CCG position). In at least one embodiment, therecruitment language includes metadata, such as one or more historicalparameters, prediction scores, weights and/or classifications. In oneexample the metadata includes a prediction score (e.g., generated from amachine learning process) that estimates a likelihood of the recruitmentlanguage in eliciting engagement. The metadata may include one or morehistorical parameters, prediction scores, weights and/orclassifications. The system can process the retrieved language andmetadata to create a training dataset of known inputs (e.g., languageitems, and the known parameters, prediction scores, impacts, and/orclassifications) and known outcomes (e.g., optimal language andengagement scores).

At step 409, the process 400 includes configuring parameters andgenerating one or more machine learning models. The machine learningmodel can be configured to evaluate the known inputs of the trainingdataset and predict optimal recruitment language for inclusion in acommunication to the candidate provided in the request. The machinelearning model can generate optimal recruitment language (e.g., languageitems, and combinations of language items, which may be sourced from theretrieved recruitment language). The machine learning model can beconfigured to calculate and assign, to each instance of optimallanguage, one or more predicted engagement scores. As described herein,an engagement score can be a metric that estimates a likelihood that aninstance or combination of optimal language item may elicit engagementfrom an associated candidate (e.g., when used to generate acommunication for the candidate). In one or more embodiments, whilegenerating the one or more machine learning models, the system mayprocess weights received from a model training process (e.g., at step412), and may weigh or evaluate each known input (or parameter producedtherefrom) according to the weights.

At step 412, the process 400 includes training the one or more machinelearning models. Training the machine learning model includes, forexample, generating and analyzing output (e.g., including one or morerecruitment language items). For example, the model service 209generates a first version of a machine learning model that is configuredto generate, from the known inputs of the training dataset, a set ofoptimal language items and predicted engagement scores. The modelservice 209 can compare the optimal language items and the predictedengagement scores to the known outcomes of the training dataset, and cancalculate one or more error metrics between the machine-learned outcomesand the known outcomes. For example, the model service 209 compares eachof the optimal language items and predicted engagement scores toanalogous language items and engagement scores in the training dataset,and, based on the comparison, calculates an error metric for eachoptimal language item.

To minimize the one or more error metrics, the system can iterativelyoptimize the first version machine learning model into one or moresecondary version machine learning models by, calculating and assigninga weight to one or more model parameters. Using the weighted modelparameters, one or more additional machine learning models can becreated and executed to generate one or more additional sets ofmachine-learned outcomes. The additional set of machine-learned outcomescan be compared to the known outcomes of the training dataset and theone or more error metrics can be recalculated. The value of one or moreparameter weights can be adjusted (e.g., increased, decreased, orremoved) to reduce the one or more error metrics. In one example, themodel service 209 generates a ranked list of parameter weights based onassociated error metric value. In this example, the model service 209increases or decreases the value of one or more top-ranked parameterweights (e.g., which may result in a reduction of the correspondingerror metric in subsequent machine learning processes). One or moresecondary machine learning models can be generated generating (e.g., atstep 404) additional machine learning models and machine-learnedoutcomes. In at least one embodiment, the system can combine one or moremachine learning models to generate an ensemble machine learning model.

The system can iteratively repeat steps 409-412, thereby continuouslytraining and/or combining the one or more machine learning models untila particular machine learning model demonstrates one or more errormetrics below a predefined threshold, or demonstrates an accuracy and/orprecision at or above one or more predefined thresholds. The system canalso process new known outcomes over time. As an example, unknownoutcomes may become known outcomes when a candidate decides whether torespond to an email, perform a job interview, or accept a job position.The actual outcome for the job candidate can be fed back into the systemto continually improve model training towards generating communicationsthat are more likely to elicit engagement.

At step 415, upon determining that the particular machine learning modelsatisfies an error metric, accuracy, and/or precision threshold, theparticular machine learning model is executed on input including atesting dataset (e.g., generated at step 402). The particular machinelearning model can predict, for each candidate of the testing dataset,engagement scores that can be used one or more language items based onlikelihood to elicit engagement. The model service 209 can rank theoptimal language items based on the predicted engagement scores and canprovide top-ranked optimal language items (e.g., items with the highestpredicted engagement scores) as language inputs to one or more naturallanguage generation algorithms, or the like. In at least one embodiment,a predicted engagement threshold is retrieved, and the communicationservice 211 selects language inputs (to the natural language generationalgorithms) by comparing each predicted engagement score to thepredicted engagement threshold, and selecting optimal language itemswhose predicted engagement score satisfies the predicted engagementthreshold.

The communication service 211 can execute one or more natural languagealgorithms to process the language inputs and generate optimalrecruitment language. The optimal recruitment language can include, butis not limited to, subject lines, body sentences and/or paragraphs,introductions, and other linguistic structures and/or patterns for usein recruitment communications. In at least one embodiment, the one ormore natural language algorithms may receive and process one or morelanguage preferences that designate a particular format for generatedoptimal recruitment language. For example, the one or more naturallanguage algorithms can receive and process a language preference forsubject lines, and, accordingly, can generate only optimal recruitmentlanguage for subject lines.

At step 418, one or more appropriate actions are performed, such as, forexample, storing one or more communications, transmitting one or morecommunications, or rendering a communication on a computing device 205.In one example, the communication service 211 automatically generates anelectronic message based on the optimal recruitment language andtransmits the electronic message to an associated candidate or to a useraccount that requested analysis of the candidate. Transmission can beperformed based at least in part on include one or more transmissionparameters from a user account. In one example, the communicationservice 211 retrieves user data 215 with which a request is associatedand extracts a transmission parameter for scheduling communications tobe automatically transmitted at a particular time and date or at aparticular frequency. In this example, the transmission parameter causesthe communication service 211 to transmit an electronic communication atthe beginning of a work week and, accordingly, communication service 211queues the electronic communication to be transmitted on the comingMonday morning.

In at least one embodiment, the computing environment 201 is configuredto monitor transmitted communications (and responses thereto) todetermine effectiveness of the transmitted communications. For example,the computing environment 201 can determine if a particular candidateengaged with a transmitted communication. In this example, in responseto determining that engagement occurred, the communication service 211can retrieve communication content including, but not limited to,subject lines, communication content, timestamps, and transmissionparameters, and the retrieved communication content can be labeled as apositive outcome. The labeled communication content can be stored andretrieved to generate training datasets for training machine learningmodels. In another example, the computing environment 201 determinesthat a particular electronic communication did not elicit engagement. Inthis example, the communication service labels communication contentassociated with the electronic communication as a negative outcome.

From the foregoing, it will be understood that various aspects of theprocesses described herein are software processes that execute oncomputer systems that form parts of the system. Accordingly, it will beunderstood that various embodiments of the system described herein aregenerally implemented as specially-configured computers includingvarious computer hardware components and, in many cases, significantadditional features as compared to conventional or known computers,processes, or the like, as discussed in greater detail herein.Embodiments within the scope of the present disclosure also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media which can be accessed by a computer, ordownloadable through communication networks. By way of example, and notlimitation, such computer-readable media can comprise various forms ofdata storage devices or media such as RAM, ROM, flash memory, EEPROM,CD-ROM, DVD, or other optical disk storage, magnetic disk storage, solidstate drives (SSDs) or other data storage devices, any type of removablenon-volatile memories such as secure digital (SD), flash memory, memorystick, etc., or any other medium which can be used to carry or storecomputer program code in the form of computer-executable instructions ordata structures and which can be accessed by a general purpose computer,special purpose computer, specially-configured computer, mobile device,etc.

When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed and considered a computer-readable medium. Combinationsof the above should also be included within the scope ofcomputer-readable media. Computer-executable instructions comprise, forexample, instructions and data which cause a general purpose computer,special purpose computer, or special purpose processing device such as amobile device processor to perform one specific function or a group offunctions.

Those skilled in the art will understand the features and aspects of asuitable computing environment in which aspects of the disclosure may beimplemented. Although not required, some of the embodiments of theclaimed systems may be described in the context of computer-executableinstructions, such as program modules or engines, as described earlier,being executed by computers in networked environments. Such programmodules are often reflected and illustrated by flow charts, sequencediagrams, exemplary screen displays, and other techniques used by thoseskilled in the art to communicate how to make and use such computerprogram modules. Generally, program modules include routines, programs,functions, objects, components, data structures, application programminginterface (API) calls to other computers whether local or remote, etc.that perform particular tasks or implement particular defined datatypes, within the computer. Computer-executable instructions, associateddata structures and/or schemas, and program modules represent examplesof the program code for executing steps of the methods disclosed herein.The particular sequence of such executable instructions or associateddata structures represent examples of corresponding acts forimplementing the functions described in such steps.

Those skilled in the art will also appreciate that the claimed and/ordescribed systems and methods may be practiced in network computingenvironments with many types of computer system configurations,including personal computers, smartphones, tablets, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, networked PCs, minicomputers, mainframe computers, and thelike. Embodiments of the claimed system are practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

An exemplary system for implementing various aspects of the describedoperations, which is not illustrated, includes a computing deviceincluding a processing unit, a system memory, and a system bus thatcouples various system components including the system memory to theprocessing unit. The computer will typically include one or more datastorage devices for reading data from and writing data to. The datastorage devices provide nonvolatile storage of computer-executableinstructions, data structures, program modules, and other data for thecomputer.

Computer program code that implements the functionality described hereintypically comprises one or more program modules that may be stored on adata storage device. This program code, as is known to those skilled inthe art, usually includes an operating system, one or more applicationprograms, other program modules, and program data. A user may entercommands and information into the computer through keyboard, touchscreen, pointing device, a script containing computer program codewritten in a scripting language or other input devices (not shown), suchas a microphone, etc. These and other input devices are often connectedto the processing unit through known electrical, optical, or wirelessconnections.

The computer that effects many aspects of the described processes willtypically operate in a networked environment using logical connectionsto one or more remote computers or data sources, which are describedfurther below. Remote computers may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically include many or all of the elements described aboverelative to the main computer system in which the systems are embodied.The logical connections between computers include a local area network(LAN), a wide area network (WAN), virtual networks (WAN or LAN), andwireless LANs (WLAN) that are presented here by way of example and notlimitation. Such networking environments are commonplace in office-wideor enterprise-wide computer networks, intranets, and the Internet.

When used in a LAN or WLAN networking environment, a computer systemimplementing aspects of the system is connected to the local networkthrough a network interface or adapter. When used in a WAN or WLANnetworking environment, the computer may include a modem, a wirelesslink, or other mechanisms for establishing communications over the widearea network, such as the Internet. In a networked environment, programmodules depicted relative to the computer, or portions thereof, may bestored in a remote data storage device. It will be appreciated that thenetwork connections described or shown are exemplary and othermechanisms of establishing communications over wide area networks or theInternet may be used.

While various aspects have been described in the context of a preferredembodiment, additional aspects, features, and methodologies of theclaimed systems will be readily discernible from the description herein,by those of ordinary skill in the art. Many embodiments and adaptationsof the disclosure and claimed systems other than those herein described,as well as many variations, modifications, and equivalent arrangementsand methodologies, will be apparent from or reasonably suggested by thedisclosure and the foregoing description thereof, without departing fromthe substance or scope of the claims. Furthermore, any sequence(s)and/or temporal order of steps of various processes described andclaimed herein are those considered to be the best mode contemplated forcarrying out the claimed systems. It should also be understood that,although steps of various processes may be shown and described as beingin a preferred sequence or temporal order, the steps of any suchprocesses are not limited to being carried out in any particularsequence or order, absent a specific indication of such to achieve aparticular intended result. In most cases, the steps of such processesmay be carried out in a variety of different sequences and orders, whilestill falling within the scope of the claimed systems. In addition, somesteps may be carried out simultaneously, contemporaneously, or insynchronization with other steps.

Aspects, features, and benefits of the claimed devices and methods forusing the same will become apparent from the information disclosed inthe exhibits and the other applications as incorporated by reference.Variations and modifications to the disclosed systems and methods may beeffected without departing from the spirit and scope of the novelconcepts of the disclosure.

It will, nevertheless, be understood that no limitation of the scope ofthe disclosure is intended by the information disclosed in the exhibitsor the applications incorporated by reference; any alterations andfurther modifications of the described or illustrated embodiments, andany further applications of the principles of the disclosure asillustrated therein are contemplated as would normally occur to oneskilled in the art to which the disclosure relates.

The foregoing description of the exemplary embodiments has beenpresented only for the purposes of illustration and description and isnot intended to be exhaustive or to limit the devices and methods forusing the same to the precise forms disclosed. Many modifications andvariations are possible in light of the above teaching.

The embodiments were chosen and described in order to explain theprinciples of the devices and methods for using the same and theirpractical application so as to enable others skilled in the art toutilize the devices and methods for using the same and variousembodiments and with various modifications as are suited to theparticular use contemplated. Alternative embodiments will becomeapparent to those skilled in the art to which the present devices andmethods for using the same pertain without departing from their spiritand scope. Accordingly, the scope of the present devices and methods forusing the same is defined by the appended claims rather than theforegoing description and the exemplary embodiments described therein.

What is claimed is:
 1. A machine learning process comprising: generatinga machine learning algorithm to predict a likelihood of a candidate asbeing a contract, contingent, or gig (CCG) based position holder;generating a training set including known CCG-based position holders andknown non-CCG-based position holders; generating, via the machinelearning algorithm, one or more machine-learned results; improvingaccuracy of the machine learning algorithm with the training set byreconfiguring the machine learning algorithm to reduce an error metricbased on a comparison between the one or more machine-learned resultsand known results; receiving a description of one or more candidates;generating, via the machine learning algorithm, a respective likelihoodof interest in a CCG class of positions for each of the one or morecandidates; and generating a respective communication to each of asubset of the one or more candidates open to the respective likelihoodof interest in the CCG class of positions for the subset above athreshold.
 2. The machine learning process of claim 1, furthercomprising: receiving a set of candidate parameters for a particularposition, the particular position corresponding to the CCG class ofpositions; and processing the set of candidate parameters to identifyone or more candidates from a set of candidates that meet the set ofcandidate parameters.
 3. The machine learning process of claim 1,further comprising: generating a ranking of the subset of the one ormore candidates based on the respective likelihood of interest in theCCG class of positions; and generating a communication based on theranking of the subset.
 4. The machine learning process of claim 1,further comprising: generating particular language designed to provoke aresponse from each of the subset of the one or more candidates; andgenerating one or more strings of text via natural language processingfor the respective communication for each of the subset of the one ormore candidates, wherein the one or more strings of text compriselanguage are based on the particular language.
 5. 6. The machinelearning process of claim 1, further comprising: receiving an indicationthat a particular candidate of the one or more candidates does notprefer the CCG class of positions; subsequent to receiving theindication, generating a change in a profile associated with theparticular candidate; generating that the change in the profileincreases a likelihood of interest in the CCG class of positions morethan or equal to a threshold amount; and in response to the changeincreasing the likelihood of interest more than or equal to thethreshold amount, adjusting the profile to facilitate communication withthe particular candidate.
 7. The machine learning process of claim 1,wherein the description of the one or more candidates is extracted fromat least one of media and investigative information.
 8. A machinelearning system comprising: a device configured to: generate a machinelearning algorithm to predict a likelihood of a candidate as being acontract, contingent, or gig (CCG) based position holder; generate atraining set including known CCG-based position holders and knownnon-CCG-based position holders; generate, via the machine learningalgorithm, one or more machine-learned results; improve accuracy of themachine learning algorithm with the training set by reconfiguring themachine learning algorithm to reduce an error metric based on acomparison between the one or more machine-learned results and knownresults; and analyze, via the machine learning algorithm, a respectivelikelihood of interest in a CCG class of positions for each of the oneor more candidates.
 9. The machine learning system of claim 8, whereinthe at least one device is further configured to exclude any candidatesfrom the one or more candidates that does not meet a predefinedthreshold.
 10. The machine learning system of claim 8, wherein the atleast one device is further configured to generate a respectivecommunication to each of a subset of the one or more candidates open tothe respective likelihood of interest in the CCG class of positions forthe subset above a threshold.
 11. The machine learning system of claim10, wherein the at least one device is further configured to: analyze arespective result associated with the respective communication for eachof the subset of the one or more candidates; and transform anothertraining set based on the respective result for each of the subset ofthe one or more candidates.
 12. A machine learning system comprising:memory; and at least one device in communication with the memory, the atleast one device being configured to: generate a machine learningalgorithm to predict a likelihood of a candidate as being a contract,contingent, or gig (CCG)-based position holder; generate a training setincluding known CCG-based position holders and known non-CCG-basedposition holders; generate, via the machine learning algorithm, one ormore machine-learned results; improve accuracy of the machine learningalgorithm with the training set by reconfiguring the machine learningalgorithm to reduce an error metric based on a comparison between theone or more machine-learned results and known results; analyze, via themachine learning algorithm, a respective likelihood of interest in a CCGclass of positions for each of one or more candidates; and generate arespective communication to each of a subset of the one or morecandidates open to the respective likelihood of interest in the CCGclass of positions for the subset above a threshold.
 13. The machinelearning system of claim 12, wherein the at least one device is furtherconfigured to: analyze a respective result associated with therespective communication for each of the subset of the one or morecandidates; and transform another training set based on the respectiveresult for each of the subset of the one or more candidates.
 14. Themachine learning system of claim 12, wherein the at least one device isfurther configured to: receive a set of candidate parameters for aparticular position, the particular position corresponding to the CCGclass of positions; and process the set of candidate parameters toidentify a candidate subset of the one or more candidates that meets theset of candidate parameters, wherein the subset of the one or morecandidates are selected from the candidate subset.
 15. The machinelearning system of claim 12, wherein the at least one device is furtherconfigured to: receive the respective likelihood of interest in the CCGclass of positions for the one or more candidates; analyze, for eachcandidate, if the respective likelihood of interest for a subset of theone or more candidates meets a threshold for a particular position; andgenerate and transmit, to a profile associated with the particularposition, a description of the subset of the one or more candidates thatmeet the threshold.
 16. The machine learning system of claim 12, whereinthe at least one device is further configured to exclude any candidatesfrom the one or more candidates that does not meet a predefinedthreshold.